Efficient parallel algorithms
A bridging model for parallel computation
Communications of the ACM
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
A fast and practical bit-vector algorithm for the longest common subsequence problem
Information Processing Letters
Parallel dynamic programming for solving the string editing problem on a CGM/BSP
Proceedings of the fourteenth annual ACM symposium on Parallel algorithms and architectures
Introduction to Algorithms
New Processor Array Architectures for the Longest Common Subsequence Problem
The Journal of Supercomputing
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII
A parallel wavefront algorithm for efficient biological sequence comparison
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII
On bit-parallel processing of multi-byte text
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
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This paper presents performance results for parallel algorithms that compute the longest common subsequence of two strings. This algorithm is a representative of a class of algorithms that compute string to string distances and has computational complexity O(n2). The parallel algorithm uses a variable grid size, runs in O(p) supersteps (synchronization phases) and has linear communication costs. We study this algorithm in BSP context, give runtime estimations and compare the predictions to experimental values measured on three different parallel architectures, using different BSP programming libraries and an efficient implementation for sequential computation. We find that using the BSP model and the appropriate optimized BSP library improves the performance over plain MPI, and that scalability can be improved by using a tuned grid size parameter.